{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from data import *\n",
    "from model import *\n",
    "from image_function import *\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'Data\\\\sample3\\\\' # image folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = data_dir #put your folder where you stored the annotated data\n",
    "save_folder = data_dir+'aug' #put your folder where you want stored the augmentation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gen_args = dict(rotation_range=1,\n",
    "                    width_shift_range=0.05,\n",
    "                    height_shift_range=0.05,\n",
    "                    shear_range=0.05,\n",
    "                    zoom_range=0.05,\n",
    "                    horizontal_flip=True,\n",
    "                    fill_mode='nearest')\n",
    "myGenerator = trainGenerator(10,data_folder,'image','mask',data_gen_args,save_to_dir = save_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yizhe\\Desktop\\Yi\\FP03.006Demo\\model.py:67: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"co...)`\n",
      "  model = Model(input = inputs, output = outputs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/50\n",
      "Found 256 images belonging to 1 classes.\n",
      "Found 256 images belonging to 1 classes.\n",
      "500/500 [==============================] - 108s 216ms/step - loss: 0.0642 - acc: 0.9885\n",
      "\n",
      "Epoch 00001: loss improved from inf to 0.06452, saving model to unet.h5\n",
      "Epoch 2/50\n",
      "500/500 [==============================] - 107s 214ms/step - loss: 0.0115 - acc: 0.9961\n",
      "\n",
      "Epoch 00002: loss improved from 0.06452 to 0.01147, saving model to unet.h5\n",
      "Epoch 3/50\n",
      "500/500 [==============================] - 110s 219ms/step - loss: 0.0104 - acc: 0.9962\n",
      "\n",
      "Epoch 00003: loss improved from 0.01147 to 0.01040, saving model to unet.h5\n",
      "Epoch 4/50\n",
      "500/500 [==============================] - 109s 217ms/step - loss: 0.0100 - acc: 0.9962\n",
      "\n",
      "Epoch 00004: loss improved from 0.01040 to 0.00996, saving model to unet.h5\n",
      "Epoch 5/50\n",
      "500/500 [==============================] - 108s 217ms/step - loss: 0.0099 - acc: 0.9963\n",
      "\n",
      "Epoch 00005: loss improved from 0.00996 to 0.00987, saving model to unet.h5\n",
      "Epoch 6/50\n",
      "500/500 [==============================] - 108s 216ms/step - loss: 0.0098 - acc: 0.9963\n",
      "\n",
      "Epoch 00006: loss improved from 0.00987 to 0.00986, saving model to unet.h5\n",
      "Epoch 7/50\n",
      "500/500 [==============================] - 109s 217ms/step - loss: 0.0097 - acc: 0.9963\n",
      "\n",
      "Epoch 00007: loss improved from 0.00986 to 0.00969, saving model to unet.h5\n",
      "Epoch 8/50\n",
      "500/500 [==============================] - 108s 216ms/step - loss: 0.0096 - acc: 0.9963\n",
      "\n",
      "Epoch 00008: loss improved from 0.00969 to 0.00960, saving model to unet.h5\n",
      "Epoch 9/50\n",
      "500/500 [==============================] - 108s 217ms/step - loss: 0.0095 - acc: 0.9964\n",
      "\n",
      "Epoch 00009: loss improved from 0.00960 to 0.00956, saving model to unet.h5\n",
      "Epoch 10/50\n",
      "500/500 [==============================] - 108s 216ms/step - loss: 0.0095 - acc: 0.9964\n",
      "\n",
      "Epoch 00010: loss improved from 0.00956 to 0.00945, saving model to unet.h5\n",
      "Epoch 11/50\n",
      "500/500 [==============================] - 109s 217ms/step - loss: 0.0094 - acc: 0.9964\n",
      "\n",
      "Epoch 00011: loss improved from 0.00945 to 0.00940, saving model to unet.h5\n",
      "Epoch 12/50\n",
      "500/500 [==============================] - 111s 221ms/step - loss: 0.0093 - acc: 0.9965\n",
      "\n",
      "Epoch 00012: loss improved from 0.00940 to 0.00930, saving model to unet.h5\n",
      "Epoch 13/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0093 - acc: 0.9964\n",
      "\n",
      "Epoch 00013: loss did not improve from 0.00930\n",
      "Epoch 14/50\n",
      "500/500 [==============================] - 110s 219ms/step - loss: 0.0091 - acc: 0.9965\n",
      "\n",
      "Epoch 00014: loss improved from 0.00930 to 0.00914, saving model to unet.h5\n",
      "Epoch 15/50\n",
      "500/500 [==============================] - 109s 217ms/step - loss: 0.0092 - acc: 0.9964\n",
      "\n",
      "Epoch 00015: loss did not improve from 0.00914\n",
      "Epoch 16/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0091 - acc: 0.9964\n",
      "\n",
      "Epoch 00016: loss improved from 0.00914 to 0.00909, saving model to unet.h5\n",
      "Epoch 17/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0091 - acc: 0.9964\n",
      "\n",
      "Epoch 00017: loss did not improve from 0.00909\n",
      "Epoch 18/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0090 - acc: 0.9964\n",
      "\n",
      "Epoch 00018: loss improved from 0.00909 to 0.00901, saving model to unet.h5\n",
      "Epoch 19/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0089 - acc: 0.9965\n",
      "\n",
      "Epoch 00019: loss improved from 0.00901 to 0.00893, saving model to unet.h5\n",
      "Epoch 20/50\n",
      "500/500 [==============================] - 110s 219ms/step - loss: 0.0090 - acc: 0.9965\n",
      "\n",
      "Epoch 00020: loss did not improve from 0.00893\n",
      "Epoch 21/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0089 - acc: 0.9965\n",
      "\n",
      "Epoch 00021: loss did not improve from 0.00893\n",
      "Epoch 22/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0089 - acc: 0.9965\n",
      "\n",
      "Epoch 00022: loss improved from 0.00893 to 0.00884, saving model to unet.h5\n",
      "Epoch 23/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0089 - acc: 0.9965\n",
      "\n",
      "Epoch 00023: loss did not improve from 0.00884\n",
      "Epoch 24/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0088 - acc: 0.9965\n",
      "\n",
      "Epoch 00024: loss improved from 0.00884 to 0.00884, saving model to unet.h5\n",
      "Epoch 25/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0089 - acc: 0.9965\n",
      "\n",
      "Epoch 00025: loss did not improve from 0.00884\n",
      "Epoch 26/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0087 - acc: 0.9965\n",
      "\n",
      "Epoch 00026: loss improved from 0.00884 to 0.00874, saving model to unet.h5\n",
      "Epoch 27/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0087 - acc: 0.9965\n",
      "\n",
      "Epoch 00027: loss improved from 0.00874 to 0.00874, saving model to unet.h5\n",
      "Epoch 28/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0088 - acc: 0.9965\n",
      "\n",
      "Epoch 00028: loss did not improve from 0.00874\n",
      "Epoch 29/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0087 - acc: 0.9965\n",
      "\n",
      "Epoch 00029: loss improved from 0.00874 to 0.00868, saving model to unet.h5\n",
      "Epoch 30/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0088 - acc: 0.9965\n",
      "\n",
      "Epoch 00030: loss did not improve from 0.00868\n",
      "Epoch 31/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0087 - acc: 0.9965\n",
      "\n",
      "Epoch 00031: loss improved from 0.00868 to 0.00867, saving model to unet.h5\n",
      "Epoch 32/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00032: loss improved from 0.00867 to 0.00867, saving model to unet.h5\n",
      "Epoch 33/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0088 - acc: 0.9965\n",
      "\n",
      "Epoch 00033: loss did not improve from 0.00867\n",
      "Epoch 34/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00034: loss improved from 0.00867 to 0.00862, saving model to unet.h5\n",
      "Epoch 35/50\n",
      "500/500 [==============================] - 110s 220ms/step - loss: 0.0087 - acc: 0.9966\n",
      "\n",
      "Epoch 00035: loss did not improve from 0.00862\n",
      "Epoch 36/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0087 - acc: 0.9966\n",
      "\n",
      "Epoch 00036: loss improved from 0.00862 to 0.00860, saving model to unet.h5\n",
      "Epoch 37/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00037: loss improved from 0.00860 to 0.00860, saving model to unet.h5\n",
      "Epoch 38/50\n",
      "500/500 [==============================] - 110s 219ms/step - loss: 0.0087 - acc: 0.9965\n",
      "\n",
      "Epoch 00038: loss did not improve from 0.00860\n",
      "Epoch 39/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00039: loss did not improve from 0.00860\n",
      "Epoch 40/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00040: loss improved from 0.00860 to 0.00857, saving model to unet.h5\n",
      "Epoch 41/50\n",
      "500/500 [==============================] - 109s 219ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00041: loss did not improve from 0.00857\n",
      "Epoch 42/50\n",
      "500/500 [==============================] - 110s 220ms/step - loss: 0.0087 - acc: 0.9965\n",
      "\n",
      "Epoch 00042: loss did not improve from 0.00857\n",
      "Epoch 43/50\n",
      "500/500 [==============================] - 112s 225ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00043: loss improved from 0.00857 to 0.00856, saving model to unet.h5\n",
      "Epoch 44/50\n",
      "500/500 [==============================] - 111s 222ms/step - loss: 0.0085 - acc: 0.9966\n",
      "\n",
      "Epoch 00044: loss improved from 0.00856 to 0.00852, saving model to unet.h5\n",
      "Epoch 45/50\n",
      "500/500 [==============================] - 108s 216ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00045: loss did not improve from 0.00852\n",
      "Epoch 46/50\n",
      "500/500 [==============================] - 111s 222ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00046: loss did not improve from 0.00852\n",
      "Epoch 47/50\n",
      "500/500 [==============================] - 111s 221ms/step - loss: 0.0085 - acc: 0.9966\n",
      "\n",
      "Epoch 00047: loss improved from 0.00852 to 0.00848, saving model to unet.h5\n",
      "Epoch 48/50\n",
      "500/500 [==============================] - 110s 219ms/step - loss: 0.0086 - acc: 0.9966\n",
      "\n",
      "Epoch 00048: loss did not improve from 0.00848\n",
      "Epoch 49/50\n",
      "500/500 [==============================] - 110s 220ms/step - loss: 0.0085 - acc: 0.9966\n",
      "\n",
      "Epoch 00049: loss did not improve from 0.00848\n",
      "Epoch 50/50\n",
      "500/500 [==============================] - 109s 218ms/step - loss: 0.0085 - acc: 0.9966\n",
      "\n",
      "Epoch 00050: loss improved from 0.00848 to 0.00848, saving model to unet.h5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x23bcb5c7390>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = unet()\n",
    "model_checkpoint = ModelCheckpoint('unet.h5', monitor='loss',verbose=1, save_best_only=True)\n",
    "model.fit_generator(myGenerator,steps_per_epoch=500,epochs=50,callbacks=[model_checkpoint], class_weight=[2.9, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\skimage\\transform\\_warps.py:105: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
      "C:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(882, 256, 256, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data, fs = load_images_from_folder(data_dir + 'Validate',  resize = True) # load validation data\n",
    "data = np.expand_dims (data, axis = 3) # keras used channel last by default\n",
    "np.shape(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23bcd1a2160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = model.predict(data)\n",
    "plt.imshow(results[230,:,:,0], cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23bcf7c0be0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "plt.subplot(121)\n",
    "plt.imshow(results[230,:,:,0]>0.001, cmap = 'gray')\n",
    "plt.subplot(122)\n",
    "plt.imshow(data[230,:,:,0], cmap = 'gray')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
